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Cheatsheet...","preview":{"posterSrc":"data:image/png;base64,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","censoredPosterSrc":"data:image/png;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAUDBAQEAwUEBAQFBQUGBwwIBwcHBw8LCw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Simple ways to optimize pandas https://github.com/softhints/python/b... Optimize datatypes of dataframe Use built-in functions Search for smart alternative Do tests Bonus tips: Use NumPy...","preview":{"posterSrc":"data:image/png;base64,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","censoredPosterSrc":"data:image/png;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAUDBAQEAwUEBAQFBQUGBwwIBwcHBw8LCwkMEQ8SEhEPERETFhwXExQaFRERGCEYGh0dHx8fExciJCIeJBweHx7/2wBDAQUFBQcGBw4ICA4eFBEUHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh7/wAARCABEAHgDASIAAhEBAxEB/8QAHQAAAQMFAQAAAAAAAAAAAAAAAAIDBAEFBggJB//EAEIQAAECBAMEBgUJBgcAAAAAAAECAwAEBREGEiEHEzFBFCJRYXGRCDJSgbEVJDQ3U1RydbNCkqHB0dIWIyUzovDx/8QAGAEBAQEBAQAAAAAAAAAAAAAAAAEDBAL/xAAcEQEBAQEBAQADAAAAAAAAAAAAARESAiExQVH/2gAMAwEAAhEDEQA/ANxpf6O3+AfCFm9jbjCJf6O3+AfCIWJ516m4bqdQl0Bb0rKOvNpPAqQhSgPMQHL2fXtNnduT7jblWcxz8rLQlbJUXUvpcKSEkaBCbW9kJHZHRyS2epZS28cX4yM2ACpxVbcWkq5ndqBb92W3dFdhgk17IMIzEuptxb9Glph1wEFS3HWw46onjdS1KJ7yYzUkCA8yxni/GGAq3QhU6ZL1/C9QnWafMVRg7qaknXVhDa3WgMikFRSCpOXU8AbA5htE+r/EX5VNforiNiebpWINn1Zfp03KVKWEs/kdYcS6gOtZuBFxdK0DwIh3Halq2bV5bqQlxVImSodh3Krwg50U1tT0okplJFQCEf7jyUH1bcyONokCVWtwlMhTrJFsvS068wfW17IRRJ2WZkEIcmghRSjTOsWsOOgPdDr1bSiZOUrfSBYKTMKSD5i/d7o6uPLDEKcKJd5TS5KVBIBAQ7ntqeYJhKpxgnSnSwHibxImKuxMoQiZlFPBBvrNqtfyi2vLaU6pTSA2g8E581veeMTiJiSJqXAP+ny5JN9SrTuGsNTTzbxBblm2LcchOvnDFx2jzguO0ecWeZDFYIpcdo84LjtHnFFTwPhBFCRY6jh2wR5r15dR5f6O3+AfCPKvSpxzN4N2Uz7dGlnpuu1ZCpKQZZZLqk5gQ46UgHRCCTfhmKRzj1RlSUSqVKUEpCASSdALRbMKTU1UaaapNAoRNuKdlWyLFtjg3fvUkBZ7Cu3KOZs5N4er+JcI11uoUWp1CkVKWVZK2nFNrTb9kjm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Computer Write This? https://www.nytimes.com/interactive/2... Part of learn python for beginners 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split string into list examples https://blog.softhints.com/python-spl... 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This video is sponsored by Brilliant. Go to https://brilliant.org/cms to sign up for free. Be one of the first 200 people 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This video is sponsored by Brilliant. Go to https://brilliant.org/cms to sign up for free. 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this video we continue working with the data frame and show how to smooth our timeseries data using the rolling filter. 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In this series we will be walking through everything you need to know to get started in Pandas! 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Python Pandas tutorial shows how to combine or merge two columns of a DataFrame into one new column using string addition. This is especially useful for dates where you have separate 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Python Pandas tutorial video teaches you how to select, slice and filter data in a DataFrame, by both rows and columns, using the index or conditionals such as Lambda functions. 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explains...","preview":{"posterSrc":"data:image/png;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAUDBAQEAwUEBAQFBQUGBwwIBwcHBw8LCwkMEQ8SEhEPERETFhwXExQaFRERGCEYGh0dHx8fExciJCIeJBweHx7/2wBDAQUFBQcGBw4ICA4eFBEUHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh7/wAARCABEAHgDASIAAhEBAxEB/8QAGwAAAgMBAQEAAAAAAAAAAAAAAAcEBQYBAwj/xAA7EAABAwMDAgMFBgQFBQAAAAABAgMEBQYRABIhBzETIlEIFEFh0RVTcYGRkhYyUqEXIzNCkyRUscHh/8QAGgEBAAMBAQEAAAAAAAAAAAAAAAIDBAEGBf/EAC8RAAEDAgQEBAYDAQAAAAAAAAEAAhEDIQQSMUEFUWFxE5Gh8BSBsbLB4QYi0fH/2gAMAwEAAhEDEQA/APoP3WL/ANsx/wASfppNdcZfUaRflDtKwnYNBhPQHajUKzIZbDLDbaiFlxakkIQgbScDJKx8NOrWG6tWlMvahVC04dVZpUitwExYsl4K8PxG5CHlMq28jxEJPbv4Z79iRV9VeD3RtVYZ6u2tFcymGqtMUAnL45ICN5IWU88I4T5gAOdU3s6v3yuo3BQb5kQ67Fhe6v0ysspQ43MaeCiC24APEQUpCgTyk5B9BymWLWmy709m9QYbkx9QkruBEp77ZbqwZEcJSweTGEceHk8FBKtw4A1XRuyzYdJatYVcVcUlr3V2WlsoQp5T7rym0gk8NhxIPP8AMtQ4wQCJC9Pr168dQZtZRasi2lNUt8NO+8wY7eNxXtxlJzwg62nQDqhcVZvm4bG6hwaW1UaQy68uSzGbbCPBWEupXt8pAB3BQx2PfjSi9n+wLlvaoXY5b1/VC1BDmIDyYoc/6gqU7tJ2LT/LtPfPfWh6l2PD6I9O6xJ/iGRXLru7NMTIW14ZRHKg5IUAVKUpSsJSVE/7/wAdEVx0o69VC4+tgo9VYgN2zVZD0amD3JtCmlZ/ycrxlROAkg/FY1ova06jXJ07lW23a6qfHTPbkKkeLAadyUKQE43Dj+Y6TF42B1WoXTCkuVCz4FNgWw4ucioR5KFSgXVpJU4A4c4UEdkjbt/HWq683TGvj/Bq5UJbV78pfvLZGUpeD7CXUEem4K/IjRFsLy6yznvZxpV+WyKdGrKqg1AqKFQ23EtuhCysBKgQArahY+Ssapa71G6qVPqXR7OtB+hpk1CiwpgTJp7ASXFw0vOncpJwCdxA/AaWHtE21Uem121y0IWU2xWZLdThII8oCC4Egeikb1oPqNp9NaqJctGtH2j7Ur9flmJTo1sQA66G1OFO6mJSnypBJ5I0Ra9HVDqd086g0mh9V6VRJFMqikhMiJGaBQkqCStCkYB2k+ZKhnHpkHW19qbqNK6eUKlw7cahCu1KSrZviId2Mo4UdpGCSopA/BWlD1/vGkdZ+oVm21YfvNQLLqmy+Y6mwVurRnAVyEoSjcVEAcn016VSo3n1G9o2pXbYVEhXBGtZxMeGia6lMfYnehCzladxUve4AD6Z7aInZ036lQ650Dfv+ZCiyJ1LhPmosNtITufZSTgADyhY2K+W75aV9iXV7QXUOju3FbX8Hx6cZC2UtuR46SlScEgBQUrHmHJPOqz2fpMywOpFzdNuoFMZp8GuQHJK4e/xGU4bUvakpJylTRcTwScpSO+qKTb3syrkuuROo11w2lqyllENagj5ZLOT+fOiL6N6PQOqbcqonqamgOx1NI9x9xZZzv3HfnYkcYx30aT/ALHNQkI6o3XQ7fq1RqlmsxlOR3ZSSnzB1IaWUnhClJK8gYyB244NEX1VrCXR1Fs5lt2C6JNYRu2uphxy4hKknI8+QMgjgpOQRnIxqh9oW46nBjRbdpzrkZMxpTkxxAwtTedobB/2hXOT3wMeuqKjhuOwyPDQzHQlKRlQSlI9MnVVap4YlX4ah47sswmJY96w7uS9Cpd1XEy6y3/mw5SG25KW8/elG8pz8Qsn561sOMxDjNxorSWWWhhCE9hzn9c5OTySSTzpT1RhUNLVUpziWZDJ3tvhQGPz7Eeo+OmnQ5S6lRodQ8FSDIZS4UgZCSRyP1zqNCuKoU8VhHYcgEyCo1Ct6g0IyDRKLTqYZKgp8xI6WvFIzgq2gZPJ7+p1ys25b9akxpVYolNqL8U5juSoyXVM8g+UqBxyAePTVt4bn3a/2nRsX/Qv9p1esq8J0aNOiPxJrDUmNIQW3mnUBSHEngpUDwQfTVGixbKQ1FZTaVCDcRxTsZAgt4ZWSCVIGPKTtTkj+kemtHsX/Qv9p0bF/wBC/wBp0RVNw23b9whkV+h02rBgqLXvkZD3hlWN23cDjOBn8NZPqbQLPp9EFWfsy2ZskLjwmnp0JBaYQSG0FasZDaEgDA7DA40wti/6FftOqy5Gau5S1IpMKBLeKgHGJ+4NOt87k5AOCeOSCNQqTlMK7D5fFbmEie3qsJToTNoW5LqkGg2Q047KjRUSKLF2IebddShYXjkYCuAFEH4jV1R3rQs9qtiLHo1JitVIRS1ToCmlqc2JKUKSkZccwonKRjB+R1S/wPXnYFafYpNIo7stcJyPTIjyvAK2HfEK1qCQEqWPLkJ4wM6spFsXE3WJFfiR4jktuuLqLEZ15SUOtrjJZUkrCfIoEEg4Pb56yh9Ube7/AKsvpupYV0guG+kDZsDcC8iRbU6LlSeses1hmvT4dDntMUp2SJEimqVKbQ25tUQpSfKlJKgUY3ZPHGdQIFI6Kzo06QxbVreFAZD8hTlIS3taPZY3IG5J7AjPOrGv25c1bEuTKiwWJMm3pVP8Np9SkJdcdCkDcUgkbRyr115XvbE6VGqUp16PFjC20Qy88TtQ428HPMAM7MJxkevbUjUqiSB787KDcPhXQ0ugnqDF+17K3sGbZi2ZFOs+LDhNshDr8ePAMXbuyElSSlOScH8vx0aqenFRmV287grDzUIoVDhsb4L5kM7k+ISnxMAKUAoEgdsgd9GrqNTOzMVjxlAUKpYOQ3nUA62SKtOHU51KZmLcMptUrYrxXCpaU8A4z3HfjTArFLmVGOqOy8/EZCfKpoDO7PHf4cY/PS26a3CimSnKdJQpbUkjwSBnY7wOR6KHB9MA6bAqz6WV+6bV8cD4n/7q7i9N7KpJ307KHA30X02gcr9/2qR+JNmW+KXIlueIwpW3YrCk57Htzg886XF7u1Wn3A4wuoSxlptQ2vKSCNuOwOByDrcS6rLamLebVOZWtWxbSWlqSrn4nt+esH1DlKl3M4pRBUhptCsfA4yR/fU+Bg/EERt+QpfyUt+GB3kfQpk22LetLpDS72uGjVG6plZmOsobcqDrceIhsqHmIJ8x2nuPjxjHOIv65KDWZ0J+zKZWaIVtkSoyqk4+hThVhIa5Jxj9ScYGOdP09k9ZLQtOPOt6jSp1vVXLzbCoYltE5xuKE+ZGcfIHg63c6FT03Z0puu5bfg23X6jUnET4jTfhJWE58F1SD/Kd5b78+fB7DH3MwpvJN9dD3tC8uGmowAW01HrKUNwWP1NoFDFbrFKrcSBgFbqpBPhg9t6UqJR+YGrO/YrdP6SdPqxDcksz6m1MVNfTJc3PFDgCc+bHA9NMidUotu1y95AszqNLfkxpTNTXMeQ7DKFk4dxgDaM5BBOEk/DOs+6w0OnnRCdUGFLpUWc4JzxQS02DKR/OrsM4Pf0OjaznZSRv52PdDRa3MAdvyFhahYvU+n25/EEykV1mmhHiKcU+rchH9SkBW9I+ZHHx1Gs21eoN3tPPW5Dq89lk7XHUyihsK77dylAE9uBp81apRbc6uXBVxZnUapzAh73oh5LkF6OU90pIxsAxgZyMeudL65Ylw1HoRYAs5ioyKe0ZSZ7VNC1KTJ8Ty+IG+e27BPqPUa6zEPcBIAnf5E8+i47DsaTcmNvmOij2J0yuys2/d66hHr7FXpTaG4EdU3wt8g8qSoKVyNpQQcgHPBOsvPTWl2HQFM0WZHdlVB6O3VhUVFU5e4pDWzd5dp4z8ca2ll0+7HLa6sU64Y1VfuBdIi72ZAU7IX5lbeOSry4xjUVzjo10xHYi6H8/82uh5zGYNx9srhYMtpFj90LPy+m/VmLAlzn6JXUsQ8+MRKJOAMkpSF5UB6pB+OqmzLcvy8TIFtx6tUUxwPGWiUUoTnsCpSgMn076c1OrVWV7aD8VVSlmMX3I/gl5Xh+EIu4J25xjdz2786zdPi1mX7ONZi2yzNdkN3e6ZrUAKLngbeMhHmKc7OPl8tBXfAmJMev/ABdNBkmCbT6JZ3HQ7wtNuI3W49SpIlpWthtb5QVBJwo7QrjkjuBo1vvaFZrEe0+m7NfElNTRRXBJEgkuhW5vhWed2MZzzo1poOzsDjHsrNXBY8gEpwVfoxYdMtqp1ak0Jz3+LHU7HUuS6vatIyCEk4zxpXRMwlowlRQrCge5A9Dr62jso8BbSkhSFZCgexB0vq10jpMqQXqdPfp45IbLYcQnPwGSCB+Z14/EuqVoJMr2GE8KhIAhKGiUdq5a61Taf4bjm0uuukYDaB3J/t+OplQ9n2kOynZD1xVZa3FlSiGWu5/LTqsWxqda8aQphxcqVJGHJC0gEpHYADsNX7sFBjkbecH/AN67hKj8PJZYlRxuXFEB9wEiba6YTLbZUzQeod005lZypphTYQT67SCM/PGoNd6MR67UDUK1elw1GWQE+NI8NagB2AyOB8hpxVyOiLNS2FeZxG/H56g62DGVpzA37BYfhKRERbuUvqj03qlTo4o0/qTdsqBgJMdxxBSoDsDxlQ+RJ1nqL04otVocu2qZ1Dr8ilNOhx6CEp8HduOFhKk4I3JPmTwSD8dNyauS3CfXDbS5KS0pTCFHAU4AdoPyJxpaWCuZSK7Ghxoc+TGnBpMyZ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this short video, you will learn 3 simple steps to plot a histogram with Pandas in Python. Blog post explaining the code: http://bit.ly/histogram_pandas Jupyter notebook with the code examples...","preview":{"posterSrc":"data:image/png;base64,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pandas and python - How to do inner and outer merge, left join and right join, left index and right index, left on and right on merge, concatenation and append, merge dataframes with 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Day 2: Python Data Type - • Python Data Type | Python Standard Data Ty... 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I use it at work and at home and I hope someone finds it helpful. Here's the 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this video, we'll be learning how to use Type Hints in Python to write self-documenting code, catch bugs earlier, and improve IDE completions. We'll start with basic type annotations 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this tutorial, we learn about cron jobs and how to schedule commands and Python scripts in the terminal via crontab (for Linux and Mac). 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Python (3.9 or 3.X or 2.X) from source code on Linux (Centos6/7/8 or Rhel6/7/8) #DIT Evolution #python #linux What is python , programming and programming language types ? Ans:- Python 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& Linux: Difficulty installing cx_Freeze for Python 3.3 Helpful? Please support me on Patreon: / roelvandepaar With thanks & praise to God, and with thanks to the many people who have made...","preview":{"posterSrc":"data:image/png;base64,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restart. step 3: restart the service now, let's create a function that will restart the specified service using the systemctl command. step 4: execute the script call the restart_service...","preview":{"posterSrc":"data:image/png;base64,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","censoredPosterSrc":"data:image/png;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAUDBAQEAwUEBAQFBQUGBwwIBwcHBw8LCwkMEQ8SEhEPERETFhwXExQaFRERGCEYGh0dHx8fExciJCIeJBweHx7/2wBDAQUFBQcGBw4ICA4eFBEUHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh7/wAARCABEAHgDASIAAhEBAxEB/8QAHQAAAQQDAQEAAAAAAAAAAAAAAAIDBAUBBgcJCP/EAD0QAAIBAwMCBQEFBAcJAAAAAAECAwQFEQAGIQcSEyIxQVEUMmFxgZEVI7HRCBYXJVKh8CQzQlWCs8HS4f/EABkBAAMBAQEAAAAAAAAAAAAAAAABAgMEBf/EACYRAAICAQMDBAMBAAAAAAAAAAABAhEhAxIxBEFRExUiYQUUUqH/2gAMAwEAAhEDEQA/APmIpSgqZJy/JDc8n4OsSS0jFD2ZIJViF9V9j+Oogjc+g1hlKjnGvQk7LhHb3MHGTj09tY9tOBAVJ7h+WiNC3HYzNn0HuNRRVjesc+2pf00wjZxTsqquSW4404tDPgl3iRQMdzH1Hrn/AD0AQMNnGDk+2lCKQ+2NSZqcEqyVMcjnPCg8fGnkioyO0RVUzDlsA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this short tutorial we will learn how to install Pandas in Python. In fact, we will install Python using two methods; by installing the scientific Python distribution Anaconda, and 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How to filter results of value_counts? https://blog.softhints.com/pandas-how... Step #1: How value_counts works Step #2: Filter value_counts with isin Step #3: Use group by and lambda to...","preview":{"posterSrc":"data:image/png;base64,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","censoredPosterSrc":"data:image/png;base64,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I show two ways, the Terminal, which is suggested by experts and the python IDE IDLE. Basic terminal instructions: • Intro to the...","preview":{"posterSrc":"data:image/png;base64,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","censoredPosterSrc":"data:image/png;base64,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